🧑‍🔬 Charlott Jakob

🏛️ Affiliations

🎓Technische Universität BerlinBerlin · Germany2024–2026 · 20 papersEmployment · Quality & Usability Labcurrent🔬German Research Centre for Artificial IntelligenceKaiserslautern · Germany2024–2026 · 13 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Retrieving Climate Change Disinformation by Narrative (2026 · 1 citation)
   Climate disinformation narratives are usually detected by classifiers built on fixed taxonomies, so new storylines go unnoticed.
2. Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemas (2026)
   Detecting propaganda in short social media posts is difficult. The texts are brief and noisy, and human annotators often disagree. Existing taxonomies list rhetorical devices such as loaded language. The authors instead propose an intent-based scheme that groups techniques by the strategic goal behind a message, such…
3. hermeneutichools at SemEval-2026 Task 4: Multiperspectivity as a Resource for Narrative Similarity Prediction (2026)
   The paper addresses SemEval-2026 Task 4 (Track A). Each item is an anchor story and two candidate stories, and a system must pick the candidate closer to the anchor. The authors treat the existence of several valid readings of a narrative as something a model can use, not a defect of the benchmark.
4. News Credibility Assessment by LLMs and Humans: Implications for Political Bias (2026)
   The paper asks whose viewpoint LLMs reflect when they judge news outlets. Earlier work compared model ratings only with expert ratings. Here the authors also compare them with public opinion split by political camp. They built a dataset of 47 outlets, 27 from the U.S. and 20 from Germany. Each outlet has expert…
5. Take It All: Ensemble Retrieval for Multimodal Evidence Aggregation (2026)
   Fact-checking claims that pair text with images, such as miscaptioned photos or fabricated infographics, requires analyzing both modalities.

🤝 Co-authors here

7 co-authors with a page, the most papers together first.

• Vera Schmitt ×5
• Max Upravitelev ×4
• Premtim Sahitaj ×4
• Veronika Solopova ×4
• Ariana Sahitaj ×3
• Sebastian Möller ×3
• Jing Yang

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Fact Extraction and VERification Workshop (FEVER): FEVER 2026
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Workshop on Ecology, Environment, and Natural Language Processing: Workshop on Ecology, Environment, and Natural Language Processing 2026
• International Workshop on Semantic Evaluation (SemEval): SemEval 2026
• Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis: Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis 2026

🔗 Cited by

Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read).

Updated 1 November 2026

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